Ride-Hail Drivers, Taxi Drivers and Multiple Jobholders: Who Takes the Most Risks and Why?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Little is known about how the use of ride-hail apps (e.g. Uber, Lyft) affects drivers’ propensity to engage in risky behaviours. Drawing on labour process theory, this study examines how algorithmic control of ride-hail drivers encourages risky driving (i.e. violating road safety rules, carrying weapons). Furthermore, the theory of work precarity is used to explain why multiple jobholders (MJHers), who work for ride-hail companies, drive taxis and hold other jobs, may be more likely to take risks while driving due to income insecurity and erratic work hours. The hypotheses are tested in a sample ( N = 191) of ride-hail drivers, taxi drivers and MJHers. The results suggest that MJHers are more likely to engage in risky driving in comparison to ride-hail and taxi drivers. Theoretical, practical and policy implications are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it